Sufficiently Important Difference for Common Cold: Severity Reduction
Bibliographic record
Abstract
PURPOSE: We undertook a study to estimate the sufficiently important difference (SID) for the common cold. The SID is the smallest benefit that an intervention would require to justify costs and risks. METHODS: Benefit-harm tradeoff interviews (in-person and telephone) assessed SID in terms of overall severity reduction using evidence-based simple-language scenarios for 4 common cold treatments: vitamin C, the herbal medicine echinacea, zinc lozenges, and the unlicensed antiviral pleconaril. RESULTS: Response patterns to the 4 scenarios in the telephone and in-person samples were not statistically distinguishable and were merged for most analyses. The scenario based on vitamin C led to a mean SID of 25% (95% confidence interval [CI] 0.23-0.27). For the echinacea-based scenario, mean SID was 32% (95% CI, 0.30-0.34). For the zinc-based scenario, mean SID was 47% (95% CI, 0.43-0.51). The scenario based on preliminary antiviral trials provided a mean SID of 57% (95% CI, 0.53-0.61). Multivariate analyses suggested that (1) between-scenario differences were substantive and reproducible in the 2 samples, (2) presence or severity of illness did not predict SID, and (3) SID was not influenced by age, sex, tobacco use, ethnicity, income, or education. Despite consistencies supporting the model and methods, response patterns were diverse, with wide spreads of individual SID values within and among treatment scenarios. CONCLUSIONS: Depending on treatment specifics, people want an on-average 25% to 57% reduction in overall illness severity to justify costs and risks of popular cold treatments. Randomized trial evidence does not support benefits this large. This model and these methods should be further developed for use in other disease entities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".